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Project overview: numerical modeling, measurements and machine learning

This project explores the effectiveness of different numerical modeling schemes and machine learning techniques in wind energy optimization. Through measurements and data analysis, the project aims to determine the most efficient combination of wind speed, spatial scheme, refinement, and time scheme. The findings will help optimize wind energy systems and minimize computational efforts.

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Project overview: numerical modeling, measurements and machine learning

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  1. Project overview: numerical modeling, measurements and machine learning Ramaz Botchorishvili, Chair for Numerical Analysis and Computational Technologies, Ivane JavakhishviliTbilisi State University(TSU) GGSWBS 2018 @ TSU, 2018, August 20-24

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  7. Multischeme: is it worth of efforts? Wind - Vmax -1 Spatial scheme - upwind -1 Refinement - 0 -1 Time scheme - Euler -1 Wind - 2*Vmax -2 Spatial scheme - MUSCL -2 Refinement - 2 -2^6 Time scheme - Euler -1 times more work=2^8 Wind - 4*Vmax -4 Spatial scheme - MUSCL -2 Refinement - 4 -2^12 Time scheme - RK2 -2 Wind - 8*Vmax -8 Spatial scheme - ENO5 -16 Refinement - 8 -2^24 Time scheme - RK4 -4 times more work=2^35 times more work=2^16 Conclusion: it is worth, if do not wish to spend 2^(xx) more efforts

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  16. Thanks for listening!

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